Summary General workflows and best practices for exoplanet detection and characterization from light curve data. Use when planning an exoplanet analysis pipeline, understanding when to use different methods, or troubleshooting detection issues.
Exoplanet Detection Workflows
This skill provides general guidance on exoplanet detection workflows, helping you choose the right approach for your data and goals.
Overview
Exoplanet detection from light curves typically involves:
Data loading and quality control
Preprocessing to remove instrumental and stellar noise
Period search using appropriate algorithms
Signal validation and characterization
Parameter estimation
Pipeline Design Principles
Key Stages
Data Loading : Understand your data format, columns, time systemQuality Control : Filter bad data points using quality flagsPreprocessing : Remove noise while preserving planetary signalsPeriod Search : Choose appropriate algorithm for signal typeValidation : Verify candidate is real, not artifactRefinement : Improve period precision if candidate is strong
Critical Decisions
What to preprocess?
Remove outliers? Yes, but not too aggressively
Remove trends? Yes, stellar rotation masks transits
How much? Balance noise removal vs. signal preservation
Which period search algorithm?
TLS : Best for transit-shaped signals (box-like dips)Lomb-Scargle : Good for any periodic signal, fast explorationBLS : Alternative to TLS, built into Astropy
What period range to search?
Consider target star type and expected planet types
Hot Jupiters: short periods (0.5-10 days)
Habitable zone: longer periods (depends on star)
Balance: wider range = more complete, but slower
After finding promising candidate
Narrow search around candidate period
Improves precision for final measurement
Choosing the Right Method
Transit Least Squares (TLS)
Searching for transiting exoplanets
Signal has transit-like shape (box-shaped dips)
You have flux uncertainties
Most sensitive for transits
Handles grazing transits
Provides transit parameters
Slower than Lomb-Scargle
Only detects transits (not RV planets, eclipsing binaries with non-box shapes)
Lomb-Scargle Periodogram
Exploring data for any periodic signal
Detecting stellar rotation
Finding pulsation periods
Quick period search
Fast
Works for any periodic signal
Good for initial exploration
Less sensitive to shallow transits
May confuse harmonics with true period
Box Least Squares (BLS)
Alternative to TLS for transits
Available in astropy
Note : TLS generally performs better than BLS for exoplanet detection.
Signal Validation
Strong Candidate (TLS)
SDE > 9 : Very strong candidateSDE > 6 : Strong candidateSNR > 7 : Reliable signal
Warning Signs
Low SDE (<6) : Weak signal, may be false positivePeriod exactly half/double expected : Check for aliasingHigh odd-even mismatch : May not be planetary transit
How to Validate
Signal strength metrics : Check SDE, SNR against thresholdsVisual inspection : Phase-fold data at candidate periodOdd-even consistency : Do odd and even transits have same depth?Multiple transits : More transits = more confidence
Multi-Planet Systems Some systems have multiple transiting planets. Strategy:
Find first candidate
Mask out first planet's transits
Search remaining data for additional periods
Repeat until no more significant signals
See Transit Least Squares documentation for transit_mask function.
Common Issues and Solutions
Issue: No significant detection (low SDE)
Check preprocessing - may be removing signal
Try less aggressive outlier removal
Check for data gaps during transits
Signal may be too shallow for detection
Issue: Period is 2x or 0.5x expected
Period aliasing from data gaps
Missing alternate transits
Check both periods manually
Look at phase-folded light curves
Check if one shows odd-even mismatch
Issue: flux_err required error Solution:
TLS requires flux uncertainties as the third argument - they're not optional!
Issue: Results vary with preprocessing
Compare results with different preprocessing
Plot each preprocessing step
Ensure you're not over-smoothing
Expected Transit Depths
Hot Jupiters : 0.01-0.03 (1-3% dip)Super-Earths : 0.001-0.003 (0.1-0.3% dip)Earth-sized : 0.0001-0.001 (0.01-0.1% dip) Detection difficulty increases dramatically for smaller planets.
Period Range Guidelines Based on target characteristics:
Hot Jupiters : 0.5-10 daysWarm planets : 10-100 daysHabitable zone :
Sun-like star: 200-400 days
M-dwarf: 10-50 days
Adjust search ranges based on mission duration and expected planet types.
Best Practices
Always include flux uncertainties - critical for proper weightingVisualize each preprocessing step - ensure you're improving data qualityCheck quality flags - verify convention (flag=0 may mean good OR bad)Use appropriate sigma - 3 for initial outliers, 5 after flatteningRefine promising candidates - narrow period search for precisionValidate detections - check SDE, SNR, phase-folded plotsConsider data gaps - may cause period aliasingDocument your workflow - reproducibility is key
References
Official Documentation
Key Papers
Hippke & Heller (2019) - Transit Least Squares paper
Kovács et al. (2002) - BLS algorithm
Lightkurve Tutorial Sections
Section 3.1: Identifying transiting exoplanet signals
Section 2.3: Removing instrumental noise
Section 3.2: Creating periodograms
Dependencies pip install lightkurve transitleastsquares numpy matplotlib scipy
